History

Dominik Jain eaab7b0a4b Improve environment factory abstractions in high-level API:

* EnvFactory now uses the creation of a single environment as
   the basic functionality which the more high-level functions build
   upon
 * Introduce enum EnvMode to indicate the purpose for which an env
   is created, allowing the factory creation process to change its
   behaviour accordingly
 * Add EnvFactoryGymnasium to provide direct support for envs that
   can be created via gymnasium.make
     - EnvPool is supported via an injectible EnvPoolFactory
     - Existing EnvFactory implementations are now derived from
       EnvFactoryGymnasium
 * Use a separate environment (which uses new EnvMode.WATCH) for
   watching agent performance after training (instead of using test
   environments, which the user may want to configure differently)

2024-01-12 17:13:42 +01:00

benchmark

Add Atari Results (#600 )

2022-04-24 20:44:54 +08:00

results

Fix Atari PPO example (#780 )

2022-12-04 12:23:18 -08:00

__init__.py

Fix critic network for Discrete CRR (#485 )

2021-11-28 23:10:28 +08:00

atari_c51.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_callbacks.py

Improve persistence handling

2023-10-18 20:44:18 +02:00

atari_dqn_hl.py

Revert "Depend on sensAI instead of copying its utils (logging, string)"

2023-11-08 19:11:39 +01:00

atari_dqn.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_fqf.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_iqn_hl.py

Revert "Depend on sensAI instead of copying its utils (logging, string)"

2023-11-08 19:11:39 +01:00

atari_iqn.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_network.py

DQNExperimentBuilder: Use IntermediateModuleFactory instead of ActorFactory

2023-10-18 20:44:18 +02:00

atari_ppo_hl.py

Revert "Depend on sensAI instead of copying its utils (logging, string)"

2023-11-08 19:11:39 +01:00

atari_ppo.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_qrdqn.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_rainbow.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_sac_hl.py

Revert "Depend on sensAI instead of copying its utils (logging, string)"

2023-11-08 19:11:39 +01:00

atari_sac.py

Feature/dataclasses (#996 )

2023-12-30 11:09:03 +01:00

atari_wrapper.py

Improve environment factory abstractions in high-level API:

2024-01-12 17:13:42 +01:00

README.md

Fix Atari PPO example (#780 )

2022-12-04 12:23:18 -08:00

README.md

Atari Environment

EnvPool

We highly recommend using envpool to run the following experiments. To install, in a linux machine, type:

pip install envpool

After that, atari_wrapper will automatically switch to envpool's Atari env. EnvPool's implementation is much faster (about 2~3x faster for pure execution speed, 1.5x for overall RL training pipeline) than python vectorized env implementation, and it's behavior is consistent to that approach (OpenAI wrapper), which will describe below.

For more information, please refer to EnvPool's GitHub, Docs, and 3rd-party report.

ALE-py

The sample speed is ~3000 env step per second (~12000 Atari frame per second in fact since we use frame_stack=4) under the normal mode (use a CNN policy and a collector, also storing data into the buffer).

The env wrapper is a crucial thing. Without wrappers, the agent cannot perform well enough on Atari games. Many existing RL codebases use OpenAI wrapper, but it is not the original DeepMind version (related issue). Dopamine has a different wrapper but unfortunately it cannot work very well in our codebase.

DQN (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters	time cost
PongNoFrameskip-v4	20	`python3 atari_dqn.py --task "PongNoFrameskip-v4" --batch-size 64`	~30 min (~15 epoch)
BreakoutNoFrameskip-v4	316	`python3 atari_dqn.py --task "BreakoutNoFrameskip-v4" --test-num 100`	3~4h (100 epoch)
EnduroNoFrameskip-v4	670	`python3 atari_dqn.py --task "EnduroNoFrameskip-v4 " --test-num 100`	3~4h (100 epoch)
QbertNoFrameskip-v4	7307	`python3 atari_dqn.py --task "QbertNoFrameskip-v4" --test-num 100`	3~4h (100 epoch)
MsPacmanNoFrameskip-v4	2107	`python3 atari_dqn.py --task "MsPacmanNoFrameskip-v4" --test-num 100`	3~4h (100 epoch)
SeaquestNoFrameskip-v4	2088	`python3 atari_dqn.py --task "SeaquestNoFrameskip-v4" --test-num 100`	3~4h (100 epoch)
SpaceInvadersNoFrameskip-v4	812.2	`python3 atari_dqn.py --task "SpaceInvadersNoFrameskip-v4" --test-num 100`	3~4h (100 epoch)

Note: The eps_train_final and eps_test in the original DQN paper is 0.1 and 0.01, but some works found that smaller eps helps improve the performance. Also, a large batchsize (say 64 instead of 32) will help faster convergence but will slow down the training speed.

We haven't tuned this result to the best, so have fun with playing these hyperparameters!

C51 (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20	`python3 atari_c51.py --task "PongNoFrameskip-v4" --batch-size 64`
BreakoutNoFrameskip-v4	536.6	`python3 atari_c51.py --task "BreakoutNoFrameskip-v4" --n-step 1`
EnduroNoFrameskip-v4	1032	`python3 atari_c51.py --task "EnduroNoFrameskip-v4 "`
QbertNoFrameskip-v4	16245	`python3 atari_c51.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	3133	`python3 atari_c51.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	6226	`python3 atari_c51.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	988.5	`python3 atari_c51.py --task "SpaceInvadersNoFrameskip-v4"`

Note: The selection of n_step is based on Figure 6 in the Rainbow paper.

QRDQN (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20	`python3 atari_qrdqn.py --task "PongNoFrameskip-v4" --batch-size 64`
BreakoutNoFrameskip-v4	409.2	`python3 atari_qrdqn.py --task "BreakoutNoFrameskip-v4" --n-step 1`
EnduroNoFrameskip-v4	1055.9	`python3 atari_qrdqn.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	14990	`python3 atari_qrdqn.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	2886	`python3 atari_qrdqn.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	5676	`python3 atari_qrdqn.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	938	`python3 atari_qrdqn.py --task "SpaceInvadersNoFrameskip-v4"`

IQN (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20.3	`python3 atari_iqn.py --task "PongNoFrameskip-v4" --batch-size 64`
BreakoutNoFrameskip-v4	496.7	`python3 atari_iqn.py --task "BreakoutNoFrameskip-v4" --n-step 1`
EnduroNoFrameskip-v4	1545	`python3 atari_iqn.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	15342.5	`python3 atari_iqn.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	2915	`python3 atari_iqn.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	4874	`python3 atari_iqn.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	1498.5	`python3 atari_iqn.py --task "SpaceInvadersNoFrameskip-v4"`

FQF (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20.7	`python3 atari_fqf.py --task "PongNoFrameskip-v4" --batch-size 64`
BreakoutNoFrameskip-v4	517.3	`python3 atari_fqf.py --task "BreakoutNoFrameskip-v4" --n-step 1`
EnduroNoFrameskip-v4	2240.5	`python3 atari_fqf.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	16172.5	`python3 atari_fqf.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	2429	`python3 atari_fqf.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	10775	`python3 atari_fqf.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	2482	`python3 atari_fqf.py --task "SpaceInvadersNoFrameskip-v4"`

Rainbow (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	21	`python3 atari_rainbow.py --task "PongNoFrameskip-v4" --batch-size 64`
BreakoutNoFrameskip-v4	684.6	`python3 atari_rainbow.py --task "BreakoutNoFrameskip-v4" --n-step 1`
EnduroNoFrameskip-v4	1625.9	`python3 atari_rainbow.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	16192.5	`python3 atari_rainbow.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	3101	`python3 atari_rainbow.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	2126	`python3 atari_rainbow.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	1794.5	`python3 atari_rainbow.py --task "SpaceInvadersNoFrameskip-v4"`

PPO (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20.2	`python3 atari_ppo.py --task "PongNoFrameskip-v4"`
BreakoutNoFrameskip-v4	441.8	`python3 atari_ppo.py --task "BreakoutNoFrameskip-v4"`
EnduroNoFrameskip-v4	1245.4	`python3 atari_ppo.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	17395	`python3 atari_ppo.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	2098	`python3 atari_ppo.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	882	`python3 atari_ppo.py --task "SeaquestNoFrameskip-v4" --lr 1e-4`
SpaceInvadersNoFrameskip-v4	1340.5	`python3 atari_ppo.py --task "SpaceInvadersNoFrameskip-v4"`

SAC (single run)

One epoch here is equal to 100,000 env step, 100 epochs stand for 10M.

task	best reward	parameters
PongNoFrameskip-v4	20.1	`python3 atari_sac.py --task "PongNoFrameskip-v4"`
BreakoutNoFrameskip-v4	211.2	`python3 atari_sac.py --task "BreakoutNoFrameskip-v4" --n-step 1 --actor-lr 1e-4 --critic-lr 1e-4`
EnduroNoFrameskip-v4	1290.7	`python3 atari_sac.py --task "EnduroNoFrameskip-v4"`
QbertNoFrameskip-v4	13157.5	`python3 atari_sac.py --task "QbertNoFrameskip-v4"`
MsPacmanNoFrameskip-v4	3836	`python3 atari_sac.py --task "MsPacmanNoFrameskip-v4"`
SeaquestNoFrameskip-v4	1772	`python3 atari_sac.py --task "SeaquestNoFrameskip-v4"`
SpaceInvadersNoFrameskip-v4	649	`python3 atari_sac.py --task "SpaceInvadersNoFrameskip-v4"`